ReAct, Reasoning and Acting augments LLMs with Tools!

Introduction

Short for Reasoning and Acting, this paper introduces a new concept that improves the performance of LLMs and also provides us with more explainability and interpretability.

The goal of AGI could be one of the most important goals for human civilization to achieve. Imagine creating artificial intelligence that could generalize to many problems. There are many interpretations of what an AGI is, and when do we say that we have achieved it?

The most promising method for AGI in the last decades was the reinforcement learning path, more specifically what DeepMind was able to achieve hard tasks, AlphaGo, AlphaStar and so many breakthroughs…

However, ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples.

With this kind of result (of course, provided there is no data leakage and we can trust the evaluation methods provided in the paper), we can no longer ignore LLMs’ potential to reason and divide complex tasks into logical steps.

The Motivation Behind The Paper

This paper starts with the idea that LLMs so far are impressive in language understanding, they have been used to generate CoT (Chain of thought) to solve some problems, and they were also used for acting and plan generation.

Although these two have been studied separately, the paper aims to combine both reasoning and acting in an interleaved manner to enhance LLM's performance.

The reason behind this idea is that if you think about how you, as a human, behave in order to execute some task.

The first step is that you’ll use “inner Speech” or you’ll write down or communicate with yourself somehow, saying “How do I execute task X? to do task X I need to first do step 1 and then do step2 and so on”

More concretely, if you were to cook up a dish in the kitchen, you could ReAct something like this:

“Now that everything is cut, I should heat up the pot of water”), to handle exceptions or adjust the plan according to the situation (“I don’t have salt, so let me use soy sauce and pepper instead”), and to realize when external information is needed (“how do I prepare dough? Let me search on the Internet”).